The Role of Technology in Quantitative Trading Research

I posted my presentation titled “The Role of Technology in Quantitative Trading Research” presented in

• HKU-HKUST-Stanford Conference in Quantitative Finance. Dec 9, 2011.
• Workshop On Recent Developments Of Financial Mathematics (REDFIN2011). Dec 13, 2011.

You can find the powerpoint here.

Abstract:

There needs a technology to streamline the quantitative trading research process. Typically, quants/traders, from idea generation to strategy deployment, may take weeks if not months. This means not only loss of trading opportunity, but also a lengthy, tedious, erroneous process marred with ad-hoc decisions and primitive tools. From the organization’s perspective, comparing the paper performances of different traders is like comparing apples to oranges. The success of the firm relies on hiring the right geniuses. Our solution is a technological process that standardizes and automates most of the mechanical steps in quantitative trading research. Creating a new trading strategy should be as easy and fun as playing Legos by assembling together simpler ideas. Consequently, traders can focus their attention on what they are supposed to be best at – imagining new trading ideas/strategies.

Excerpts:

• In reality, the research process for a quantitative trading strategy, from conceptual design to actual execution, is very time consuming, e.g., months. The backtesting step, in the broadest sense, takes the longest time. There are too many details that we can include in the backtesting code. To just name a few, data cleaning and preparation, mathematics algorithms, mock market simulation, execution and slippage assumptions, parameter calibration, sensitivity analysis, and worst of all, debugging. In practice, most people will ignore many details and make unfortunate “approximation”. This is one major reason why real and paper p&l’s are different.
• Before AlgoQuant, there is no publicly available quantitative trading research platform that alleviates quants/traders from coding up those “infrastructural” components. Most of the existing tools are either lacking extensive built-in math libraries, or lacking modern programming language support, or lacking plug-and-play “trading toolboxes”.
• Technology can change the game by enhancing productivity. Imagine there is a system that automates and runs in a parallel grid of 100s of CPUs for you all those tedious and mundane tasks, data cleaning, mock market, calibration, and mathematics. You can save 80% of coding time and can focus your attention to trading ideas and analysis. Jim, using Matlab, may find a successful trading strategy in 3 months. You, equipped with the proper technology, may find 3 strategies in a month! The success of a hedge fund shall no longer rely on hiring genius.
• After we code up a strategy and choose a parameter set, there is a whole suite of analysis that we can go through and many measures that we can compute to evaluate the strategy. For instance, we can see how the strategy performs for historical data, simulated data generated from Monte Carlo simulation (parametric) or bootstrapping (non-parametric), as well as scenario data (hand crafted). We can construct the p&l distribution (it is unfortunate that historical p&l seems to be the popular performance measure; we traders do not really care about what we could make in the past but care only about our bonuses in the future; so what we really want to see is the future p&l distribution for uncertainty not historical p&l); we can do sensitivity analysis of parameters; we can compute the many performance statistics. All these are very CPU-intensive tasks. Using AlgoQuant, you simply feed your strategy into the system. AlgoQuant runs all these tasks on a parallel grid and generates a nice report for you.
• The academic community publishes very good papers on quantitative trading strategies. Unfortunately they are by-and-large unexplored. First, they are very difficult to understand because they are written for peer reviewers not laymen. Second, they are very difficult to reproduce because most authors do not publish source code. Third, they are very difficult to apply in real trading because the source code is not meant for public use, even if available.